move performance to docling page
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April M
parent
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2 changed files with 54 additions and 50 deletions
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@ -78,4 +78,43 @@ If you want to use OpenRAG's built-in pipeline instead of Docling serve, set `DI
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The built-in pipeline still uses the Docling processor, but uses it directly without the Docling Serve API.
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For more information, see [`processors.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58).
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For more information, see [`processors.py` in the OpenRAG repository](https://github.com/langflow-ai/openrag/blob/main/src/models/processors.py#L58).
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## Performance expectations
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On a local VM with 7 vCPUs and 8 GiB RAM, OpenRAG ingested approximately 5.03 GB across 1,083 files in about 42 minutes.
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This equates to approximately 2.4 documents per second.
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You can generally expect equal or better performance on developer laptops and significantly faster on servers.
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Throughput scales with CPU cores, memory, storage speed, and configuration choices such as embedding model, chunk size and overlap, and concurrency.
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This test returned 12 errors (approximately 1.1%).
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All errors were file-specific, and they didn't stop the pipeline.
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Ingestion dataset:
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* Total files: 1,083 items mounted
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* Total size on disk: 5,026,474,862 bytes (approximately 5.03 GB)
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Hardware specifications:
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* Machine: Apple M4 Pro
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* Podman VM:
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* Name: `podman-machine-default`
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* Type: `applehv`
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* vCPUs: 7
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* Memory: 8 GiB
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* Disk size: 100 GiB
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Test results:
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```text
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2025-09-24T22:40:45.542190Z /app/src/main.py:231 Ingesting default documents when ready disable_langflow_ingest=False
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2025-09-24T22:40:45.546385Z /app/src/main.py:270 Using Langflow ingestion pipeline for default documents file_count=1082
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...
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2025-09-24T23:19:44.866365Z /app/src/main.py:351 Langflow ingestion completed success_count=1070 error_count=12 total_files=1082
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```
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Elapsed time: ~42 minutes 15 seconds (2,535 seconds)
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Throughput: ~2.4 documents/second
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@ -1,23 +1,26 @@
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---
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title: What is OpenRAG?
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slug: /
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hide_table_of_contents: true
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---
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OpenRAG is an open-source package for building agentic RAG systems that integrates with a wide range of orchestration tools, vector databases, and LLM providers.
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OpenRAG connects and amplifies three popular, proven open-source projects into one powerful platform:
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* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, vector databases, and a growing library of AI tools.
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* [Langflow](https://docs.langflow.org): Langflow is a versatile tool for building and deploying AI agents and MCP servers. It supports all major LLMs, vector databases, and a growing library of AI tools.
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* [OpenSearch](https://docs.opensearch.org/latest/): OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite that makes it easy to ingest, search, visualize, and analyze data.
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* [Docling](https://docling-project.github.io/docling/): Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
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* [Docling](https://docling-project.github.io/docling/): Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
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OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing, with opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from popular sources like Google Drive, OneDrive, and Sharepoint.
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OpenRAG builds on Langflow's familiar interface while adding OpenSearch for vector storage and Docling for simplified document parsing. It uses opinionated flows that serve as ready-to-use recipes for ingestion, retrieval, and generation from familiar sources like Google Drive, OneDrive, and SharePoint.
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What's more, every part of the stack is swappable. Write your own custom components in Python, try different language models, and customize your flows to build an agentic RAG system.
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What's more, every part of the stack is interchangeable: You can write your own custom components in Python, try different language models, and customize your flows to build a personalized agentic RAG system.
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Ready to get started? [Install OpenRAG](/install) and then run the [Quickstart](/quickstart) to create a powerful RAG pipeline.
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:::tip
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Ready to get started? Try the [quickstart](/quickstart) to install OpenRAG and start exploring in minutes.
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:::
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## OpenRAG architecture
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@ -43,51 +46,13 @@ flowchart TD
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ext --> backend
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```
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The **OpenRAG Backend** is the central orchestration service that coordinates all other components.
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<br/>
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* The **OpenRAG Backend** is the central orchestration service that coordinates all other components.
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**Langflow** provides a visual workflow engine for building AI agents, and connects to **OpenSearch** for vector storage and retrieval.
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* **Langflow** provides a visual workflow engine for building AI agents, and connects to **OpenSearch** for vector storage and retrieval.
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**Docling Serve** is a local document processing service managed by the **OpenRAG Backend**.
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* **Docling Serve** is a local document processing service managed by the **OpenRAG Backend**.
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**Third Party Services** like **Google Drive** connect to the **OpenRAG Backend** through OAuth authentication, allowing synchronication of cloud storage with the OpenSearch knowledge base.
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* **External connectors** integrate third-party cloud storage services through OAuth authenticated connections to the **OpenRAG Backend**, allowing synchronization of external storage with your OpenSearch knowledge base.
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The **OpenRAG Frontend** provides the user interface for interacting with the system.
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## Performance expectations
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On a local VM with 7 vCPUs and 8 GiB RAM, OpenRAG ingested approximately 5.03 GB across 1,083 files in about 42 minutes.
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This equates to approximately 2.4 documents per second.
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You can generally expect equal or better performance on developer laptops and significantly faster on servers.
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Throughput scales with CPU cores, memory, storage speed, and configuration choices such as embedding model, chunk size and overlap, and concurrency.
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This test returned 12 errors (approximately 1.1%).
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All errors were file‑specific, and they didn't stop the pipeline.
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Ingestion dataset:
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* Total files: 1,083 items mounted
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* Total size on disk: 5,026,474,862 bytes (approximately 5.03 GB)
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Hardware specifications:
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* Machine: Apple M4 Pro
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* Podman VM:
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* Name: `podman-machine-default`
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* Type: `applehv`
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* vCPUs: 7
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* Memory: 8 GiB
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* Disk size: 100 GiB
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Test results:
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```text
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2025-09-24T22:40:45.542190Z /app/src/main.py:231 Ingesting default documents when ready disable_langflow_ingest=False
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2025-09-24T22:40:45.546385Z /app/src/main.py:270 Using Langflow ingestion pipeline for default documents file_count=1082
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...
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2025-09-24T23:19:44.866365Z /app/src/main.py:351 Langflow ingestion completed success_count=1070 error_count=12 total_files=1082
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```
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Elapsed time: ~42 minutes 15 seconds (2,535 seconds)
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Throughput: ~2.4 documents/second
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* The **OpenRAG Frontend** provides the user interface for interacting with the platform.
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